Secure facial biometric authentication in smart cities using multimodal methodology.

Area of Science:

• Computer Science
• Artificial Intelligence
• Cybersecurity

Background:

• Facial biometric security is critical in smart cities for protecting citizen data and preventing unauthorized access.
• Existing systems face challenges with spoofing attacks and secure data transmission.
• Need for robust authentication methods that combine feature extraction and cryptographic security.

Purpose of the Study:

• To propose a multimodal deep learning model integrated with a cryptographic framework for enhanced facial biometric authentication.
• To secure facial data against spoofing attacks and ensure privacy during transmission in smart city networks.
• To evaluate the model's performance and compare it with traditional methods.

Main Methods:

• Utilized a multimodal deep learning approach combining Convolutional Neural Network (CNN) for low-level feature extraction and Residual Network (ResNet-50) for high-level semantic pattern identification.
• Integrated ElGamal cryptography to secure extracted facial features and ensure data privacy.
• Trained and evaluated the model on the CelebA Faces dataset.

Main Results:

• Achieved a facial mapping prediction accuracy of 97.1% with a low mean score loss of 0.04.
• Demonstrated superior performance compared to traditional models: 1.2% higher accuracy than CNN, 2.2% higher than ResNet-50, and 1.1% higher than the Brakerski-Gentry-Vaikuntanathan algorithm.
• Effectively handled spoofing attacks and ensured secure data transmission.

Conclusions:

• The proposed fused multimodal approach significantly enhances facial biometric security in smart city environments.
• The combination of CNN, ResNet-50, and ElGamal cryptography provides a robust solution for preventing unauthorized access and ensuring data privacy.
• The model is well-suited for futuristic smart city applications requiring advanced security features.